Suicide detection in Chile: proposing a predictive model for suicide risk in a clinical sample of patients with mood disorders

Abstract

Objective: To analyze suicidal behavior and build a predictive model for suicide risk using data mining (DM) analysis. Methods: A study of 707 Chilean mental health patients (with and without suicide risk) was carried out across three healthcare centers in the Metropolitan Region of Santiago, Chile. Three hundred fortythree variables were studied using five questionnaires. DM and machine-learning tools were used via the support vector machine technique. Results: The model selected 22 variables that, depending on the circumstances in which they all occur, define whether a person belongs in a suicide risk zone (accuracy = 0.78, sensitivity = 0.77, and specificity = 0.79). Being in a suicide risk zone means patients are more vulnerable to suicide attempts or are thinking about suicide. The interrelationship between these variables is highly nonlinear, and it is interesting to note the particular ways in which they are configured for each case. The model shows that the variables of a suicide risk zone are related to individual unrest, personal satisfaction, and reasons for living, particularly those related to beliefs in one's own capacities and coping abilities. Conclusion: These variables can be used to create an assessment tool and enables us to identify individual risk and protective factors. This may also contribute to therapeutic intervention by strengthening feelings of personal well-being and reasons for staying alive. Our results prompted the design of a new clinical tool, which is fast and easy to use and aids in evaluating the trajectory of suicide risk at a given moment.

Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample.

“…The progression of these analyses is detailed in the Results section below, where we mention our prior work (Barros et al, 2017;Morales et al, 2017).…”

“…Within the category of "new factors," we include groups of variables that operate simultaneously. In other words, a new factor might constitute a configuration of variables that emerge at a particular time (Maino et al, 2018; Barros et al, 2017;Morales et al, 2017). While there is still a lack of reliable tools to predict suicide, certain factors that are predominant when a person attempts to end his or her life can be established (WHO, 2002;Verrocchio et al, 2016).…”

“…We used the structure of the data from our first two DM experiments (SVM and DT) to explore the findings that emerged regarding the difference between protective and risk factors (Barros et al, 2017;Morales et al, 2017). Subsequently, we delved deeper into the dependence relationships among the variables by using the Bayesian network technique (Sucar & Tonantzintla, 2006).…”

Recognizing states of psychological vulnerability to suicidal behavior: a Bayesian network of artificial intelligence applied to a clinical sample

“…Within the category of "new factors, " we include groups of variables that operate simultaneously. In other words, a new factor might constitute a configuration of variables that emerge at a particular time [5,23,25]. While there is still a lack of reliable tools to predict suicide, certain factors that are predominant when a person attempts to end his or her life can be established [36,42].…”

“…We used the structure of the data from our first two DM experiments (SVM and DT) to explore the findings that emerged regarding the difference between protective and risk factors [5,25]. Subsequently, we delved deeper into the dependence relationships among the variables by using the Bayesian network technique [33].…”